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Job Summary
A leader in the Banking and Financial space is on the lookout for a Data Engineer to join their team. The purpose of the Data Engineer is to leverage their data expertise and data related technologies, in line with the Companyâ€s Data Architecture Roadmap, to advance technical thought leadership for the Enterprise, deliver fit for purpose data products, and support data initiatives. In addition, Data Engineers enhance the data infrastructure of the bank to enable advanced analytics, machine learning and artificial intelligence by providing clean, usable data to stakeholders. They also create data pipelines, Ingestion, provisioning, streaming, self service, API and solutions around big data that support the Bank's strategy to become a data driven organisation. Software Engineers. Essential Qualifications - NQF Level Advanced Diplomas/National 1st Degrees Preferred Qualification Field of Study: BCom, BSc, BEng Preferred Certifications Cloud (Azure, AWS), DEVOPS or Data engineering certification. Any Data Science certification will be an added advantage, Coursera, Udemy, SAS Data Scientist certification, Microsoft Data Scientist. Minimum Experience Level Total number of years of experience:3 - 6 years Type of experience: Experienced at working independently within a squad and has the demonstrated knowledge and skills to deliver data outcomes without supervision. Experience designing, building, and maintaining data warehouses and data lakes. Experience with big data technologies such as Hadoop, Spark, and Hive. Experience with programming languages such as Python, Java, and SQL. Experience with relational databases and NoSQL databases. Experience with cloud computing platforms such as AWS, Azure, and GCP. Experience with data visualization tools.  Result-driven, analytical creative thinker, with demonstrated ability for innovative problem solving.   Technical / Professional Knowledge Cloud Data Engineering (Azure , AWS, Google) Data Warehousing Databases (PostgreSQL, MS SQL, IBM DB2, HBase, MongoDB) Programming (Python, Java, SQL) Data Analysis and Data Modelling Data Pipelines and ETL tools (Ab Initio, ADB, ADF, SAS ETL) Agile Delivery Problem solving skills Job Responsibilities Responsible for the maintenance, improvement, cleaning, and manipulation of data in the bank's operational and analytics databases. Data Infrastructure: Build and manage scalable, optimised, supported, tested, secure, and reliable data infrastructure eg using Infrastructure and Databases (DB2, PostgreSQL, MSSQL, HBase, NoSQL, etc), Data Lakes Storage (Azure Data Lake Gen 2), Cloud-based solutions (SAS , Azure Databricks, Azure Data Factory, HDInsight), Data Platforms (SAS, Ab Initio, Denodo, Netezza, Azure Cloud). Ensure data security and privacy in collaboration with Information Security, CISO and Data Governance Data Pipeline Build (Ingestion, Provisioning, Streaming and API): Build and maintain data pipelines to:  create data pipelines for data integration (Data Ingestion, Data Provisioning and Data Streaming) utilising both On Premise tool sets and Cloud Data Engineering tool sets efficiently extract data (Data Acquisition) from Golden Sources, Trusted sources and Writebacks with data integration from multiple sources, formats and structures load the Companyâ€s Data Warehouse (Data Reservoir, Atomic Data Warehouse, Enterprise Data Mart) provide data to the respective Lines of Business Marts, Regulatory Marts and Compliance Marts through self service data virtualisation provide data to applications or Companyâ€s Data consumers transform data to a common data model for reporting and data analysis, and to provide data in a consistent, useable format to Companyâ€s data stakeholders handle big data technologies (Hadoop), streaming (KAFKA) and data Replication (IBM Inphosphere Data Replication) drive utilisation of data integration tools ( Ab Initio) and Cloud data integration tools (Azure Data Factory and Azure Data Bricks) Data Modelling and Schema Build: In collaboration with Data Modellers, create data models and database schemas on the Data Reservoir, Data Lake, Atomic Data Warehouse and Enterprise Data Marts. Companyâ€s Data Warehouse Automation: Automate, monitor and improve the performance of data pipelines. Collaboration: Collaborate with Data Analysts, Software Engineers, Data Modelers, Data Scientistsm Scrum Masers and Data Warehouse teams as part of a squad to contribute to the data architecture detail designs and take ownership of Epics end-to-end and ensure that data solutions deliver business value. Data Quality and Data Governance: Ensure that reasonable data quality checks are implemented in the data pipelines to maintain a high level of data accuracy, consistency and security. Performance and Optimisation: Ensure the performance of the Companyâ€s data warehouse, integration patterns, batch and real time jobs, streaming and API's. API Development: Build API's that enable the Data Driven Organisation, ensuring that the data warehouse is optimised for API's by collaborating with Software Engineers.
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